Earnings Attribution Analysis

Earnings Attribution Analysis

Goal: Determine WHY a stock moved after an 8-K filing by calculating the SURPRISE (actual vs expected).

Thinking: ALWAYS use ultrathink for maximum reasoning depth.

End Goal: Build company-specific driver understanding for real-time prediction accuracy.

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Core Principles

1. Comprehensiveness Over Speed: Query ALL relevant data sources
2. Evidence-Based Claims Only: Every claim must cite a source
3. Surprise-Focused: Stock moves on actual vs expectations, not absolute results
4. Learn from History: Each analysis builds company-specific knowledge
5. Self-Audit: Validate all claims have sources before completing

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Confidence Levels

• High: Multiple sources agree, clear fundamental reason, unambiguous evidence
• Medium: Sources partially agree, conclusion fits with caveats
• Insufficient: Sources conflict, missing critical data, no clear driver

If evidence is insufficient, say so. Never fabricate certainty.

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Resources

• Output format: output_template.md
• Evidence audit checklist: evidence_audit.md
• Usage examples: examples.md
• Self-improvement: update-skills.md
• Known data gaps: data_gaps.md

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Neo4j Subagents

CRITICAL: Do NOT write Cypher queries. Describe what data you need in natural language. Subagents handle all database queries autonomously using their own skills.

Use Task tool to spawn these specialized subagents:

Iterative Refinement: Subagents return agent_id with results. For follow-up queries on the same company, use resume: <agent_id> instead of spawning new—agent preserves context (ticker, dates, previous queries). Spawn new agent only for different company or unrelated domain.

Resume prompt examples: "Only 2 articles, expand to ±5 days", "Also get EX-99.1 exhibit text", "What did analysts ask in Q&A?"

Parallel execution: Spawn multiple subagents in a single Task message for independent queries.

PIT filtering (prediction mode): Prefix prompt with [PIT: {filing_datetime}] to restrict results to before that time.

Skill Updates: If a subagent response contains SKILL_UPDATE_NEEDED block, execute the update immediately using Edit tool—no permission needed. This includes schema corrections, property name fixes, and data gaps. Do not skip or defer these updates.

Response Validation: When receiving subagent data, verify each value has citation format: (Source: {NodeType}:{identifier}, {date}). If citation missing:

1. Use resume: <agent_id> to ask subagent to re-provide with source
2. Do NOT add unsourced data to Evidence Ledger
3. Note "Data unavailable" if subagent cannot cite source

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Workflow (11 Steps)

Use TodoWrite to track progress. Mark each step in_progress before starting, completed immediately after.

Rules: Step 1 always first. Steps 2-5 case-by-case sequencing. Steps 6-11 sequential. Step 11 is MANDATORY.

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Step 1: Data Inventory

Query what data exists before making claims. Use neo4j-report subagent.

Check for: News count, Transcript exists, XBRL reports count, Dividends, Splits.

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Step 2: Get Report and Returns

Get filing details and price reaction. Use neo4j-report subagent.

Returns to capture:

• Macro-adjusted (vs SPY) - primary
• Sector-adjusted - context (may be null)
• Industry-adjusted - context (may be null)

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Step 3: Get Consensus Estimates

Always query Perplexity - do not rely on Neo4j News for consensus.

mcp__perplexity__perplexity_search:
  query: "{ticker} Q{quarter} FY{year} EPS revenue estimate consensus before {date}"

Source preference for Evidence Ledger:

1. Best: Perplexity pre-filing search (explicit consensus before event)
2. Acceptable: News headline citing estimate (note: post-filing, weaker provenance)
3. Unacceptable: Unsourced or inferred values

If using News as consensus source, cite it accurately in Evidence Ledger—do not claim Perplexity in Data Sources Used.

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Step 4: Query Neo4j Sources

Query based on Data Inventory results. Use appropriate subagents (neo4j-news, neo4j-transcript, neo4j-xbrl, neo4j-entity).

4A: News

• Start ±2 trading days; expand to ±5 if <3 items
• Filter: WHERE r.daily_stock IS NOT NULL (data quality)
• Extract: EPS vs estimate, guidance, analyst reactions

4B: Transcript (Item 2.02 only)

• Get Prepared Remarks + Q&A exchanges
• Look for: analyst concerns, management hedging, specific numbers

4C: Historical XBRL

• Last 4 quarters of 10-K/​10-Q data
• Look for: EPS trend, revenue trend, margin changes

4D: Dividends

• Cuts = bearish, Raises = bullish, Special = one-time

4E: Splits

• Rarely explain fundamental reactions

4F: Verify Sub-Agent Results

Check Coverage for adequate search. If Coverage missing or unclear, ask sub-agent to clarify. Re-query if gaps seem wrong (max 2 follow-ups). Check cross-domain consistency before synthesis.

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Step 5: Query Perplexity

Use when Neo4j doesn't provide complete picture.

Query Principles:

• Be specific about time (exact date, not "recently")
• Include company name AND ticker
• For consensus: specify fiscal quarter AND year
• For "why stock moved": include date AND direction

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Step 6: Synthesize

Calculate SURPRISE for each metric using these exact formulas:

Surprise Calculation Formulas

EPS Surprise % = ((Actual - Consensus) / |Consensus|) × 100
Revenue Surprise % = ((Actual - Consensus) / |Consensus|) × 100

Guidance Surprise:

1. Calculate midpoint of guidance range: (Low + High) / 2
2. Compare to consensus: ((Midpoint - Consensus) / |Consensus|) × 100
3. If range width changed significantly, note this separately

Rounding: All percentages to 2 decimal places.

Example Calculation

Actual EPS: $2.03
Consensus EPS: $1.98
Surprise = (($2.03 - $1.98) / |$1.98|) × 100 = +2.53%

Guidance Range: $12.00 - $13.50
Midpoint: $12.75
Consensus: $13.22
Surprise = (($12.75 - $13.22) / |$13.22|) × 100 = -3.56%

Surprise Analysis Table

Rank surprises by absolute magnitude. Largest surprise typically explains the move.

Attribution Structure

Primary Driver (High Confidence)

• What: [Descriptive name]
• Surprise: Expected $X, Got $Y (Z% surprise)
• Evidence: [Source citation]

Contributing Factor(s) (if applicable)

• What: [Name]
• Evidence: [Source]

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Evidence Extraction

Capture evidence immediately when reading each source. Populate the Evidence Ledger in output_template.md as you go; every numeric claim must appear there.

Strict Rules for Numbers (Used in Surprise Calculations)

Any number used to calculate surprise MUST have:

1. Exact value (not rounded or paraphrased)
2. Explicit source with timestamp/​date
3. Format: {metric}: ${exact_value} (Source: {source_name}, {date})

Examples:

Consensus EPS: $1.98 (Source: Perplexity, pre-filing)
Actual EPS: $2.03 (Source: 8-K EX-99.1, 2023-11-02)
Guidance midpoint: $12.75 (Source: 8-K EX-99.1, calculated from $12.00-$13.50)

If you cannot cite exact value + source, do NOT use the number in surprise calculations.

Flexible Rules for Qualitative Evidence

For non-numeric observations, paraphrasing is acceptable with source:

Management tone: Cautious on near-term demand (Source: Transcript Q&A #3)
Analyst concern: Margin pressure (Source: News "ROK Beats but Guides Lower")

Sources: News: "{headline}", Transcript Q&A #N, Transcript Prepared Remarks, Exhibit EX-99.1, Perplexity search, XBRL: {report_id}

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Output

See output_template.md for full report format.

Save to: earnings-analysis/​Companies/​{TICKER}/​{accession_no}.md

Required sections:

1. Report Metadata
2. Returns Summary
3. Evidence Ledger
4. Executive Summary
5. Surprise Analysis
6. Attribution (Primary + Contributing)
7. Data Sources Used
8. Confidence Assessment
9. Historical Context

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Core Rules

1. Data Inventory First: Know what data exists before making claims
2. Surprise-Based: Stock moves on actual vs expected
3. Evidence Required: Every claim needs a cited source
4. Perplexity for Consensus: Always query for market expectations
5. Self-Audit: Validate all claims have sources
6. Learn and Record: Store company-specific learnings
7. Honest Confidence: State "Insufficient" when evidence doesn't support
8. No XBRL for 8-K: Use historical 10-K/​10-Q XBRL for trends

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Perplexity Tools

Note: For SEC filings search, use utils/​perplexity_search.py:perplexity_sec_search().

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Conflict Resolution Guidelines

When sources conflict, use these principles (guidelines, not rigid rules):

Source Reliability Hierarchy

Driver Priority (Typical, Not Absolute)

• Forward-looking (guidance) usually > Backward-looking (EPS)
• BUT: If EPS miss is >10%, it can dominate
• AND: Validate against actual price reaction direction

Conflict Handling

If News says "beat" but Transcript reveals "guidance cut":
→ Trust Transcript (higher reliability)
→ Note the conflict in analysis

If two News sources give different consensus numbers:
→ Use the one with explicit source attribution
→ If neither has clear attribution, note both values and the discrepancy
→ Do NOT average (averages have no direct source)

Rule: When in doubt, note the conflict explicitly rather than silently choosing one.

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Data Quality Guardrails

News Anomaly Filter (REQUIRED)

~1,746 News→Company relationships (~0.9%) have daily_industry but no daily_stock. Always filter:

WHERE r.daily_stock IS NOT NULL

Missing Returns Handling

If a report has no return data (pf.daily_stock IS NULL):

• Flag as incomplete data
• Do NOT use for driver sensitivity calculations
• Can still analyze qualitatively

Duplicate News Detection

If multiple news items have nearly identical titles (>80% similarity):

• Use the earliest timestamp
• Treat as single data point, not confirmation

Stale Consensus Warning

When Perplexity returns consensus estimates:

• Verify the date/​quarter matches your target
• "Q3 2023 consensus" is wrong if you need Q4 2023
• Re-query with more specific date if unclear

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Company-Specific Learning

After each analysis, update: earnings-analysis/​Companies/​{TICKER}/​learnings.md

See output_template.md for learnings format.

Gating rule: Only update learnings.md after the Evidence Ledger is complete and evidence_audit.md passes.

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Session & Subagent History (Shared CSV)

History file: .claude/​shared/​earnings/​subagent-history.csv (shared with earnings-prediction)

Format: See subagent-history.md for full documentation.

accession_no,skill,created_at,primary_session_id,agent_type,agent_id,resumed_from
0001514416-24-000020,attribution,2026-01-13T10:30:00,0415feb7,primary,,
0001514416-24-000020,attribution,2026-01-13T10:31:05,0415feb7,neo4j-entity,abc12345,

On analysis start:

1. Read CSV (create with header if doesn't exist)
2. Append primary row: {accession},attribution,{timestamp},{session_id},primary,,

Before calling a subagent:

1. Query latest agent ID: grep "{accession}" | grep ",{agent_type}," | tail -1 | cut -d',' -f6
2. If agent ID exists → can use resume: <id> in Task call
3. If want fresh session → proceed without resume

After each subagent completes:

1. Extract agentId from Task response
2. Append row: {accession},attribution,{timestamp},{session_id},{agent_type},{agent_id},{resumed_from}

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Step 10: Mark Completed

After successful analysis (report saved, audit passed, learnings updated):

1. Update tracking CSV: Set completed=TRUE for this accession_no in earnings-analysis/​8k_fact_universe.csv
2. Update predictions CSV: If a prediction exists for this accession_no in earnings-analysis/​predictions.csv, fill in:
   • actual_direction: up/​down based on daily_stock return
   • actual_magnitude: small/​medium/​large based on |return|
   • actual_return: the actual daily_stock percentage
   • correct: TRUE if direction matches, FALSE otherwise
3. Verify: Confirm rows are updated correctly

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Step 11: Build Thinking Index (MANDATORY - DO NOT SKIP)

This step is REQUIRED. Execute this exact command:

python3 scripts/​build-thinking-index.py {accession_no}

Replace {accession_no} with the actual accession number from your analysis (e.g., 0001234567-24-000001).

This extracts thinking from all sessions and sub-agents and saves to Obsidian.

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Version 2.2 | 2026-01-16 | Added Step 11: mandatory thinking index build